The Challenge aimed to solve a patient re-activation problem for Cleveland Clinic. Sylvia Morrison and Brian Hoar of the Database Group at Cleveland Clinic joined the three finalists at DMA13 to present their findings, methodology and models.

“We’re extremely proud of our entire analytics team at DataLab for winning yet another DMA Challenge. We’d like to thank all the parties involved to help make this year’s competition possible. We look forward to competing again in the next challenge,” said Aaron Davis, EVP Analytics at DataLab USA.

The Challenge:

Cleveland Clinic embraces a “patient first” approach, and believes that their high touch and exemplary medical pedigree give them an advantage over other providers in the area. They already send a follow up postcard and email message to patients in certain clinical categories, but wanted to improve the conversion-to-next appointment rate and engagement with the Clinic. “We want to leverage the ’empathy’ factor and build a relationship with our patients,” Sylvia said. “Marketing, after all, is interested in driving volume and our objective is to shorten the interval between encounters – just like retail.”

The Challenge participants were given a Patient Reactivation file segmented by:

Select patients from 13-36 months, no treatment code visit in last 12 months.

Of the 240 entries, the industry judges and academic judges from NORC at the University of Chicago looked for solutions that demonstrated real innovation. “Innovation is easy to define but hard to pin down,” said Robert Montgomery, NORC director and a judge for the program. The judges defined innovation as:

Uses standard solutions when they work best

Uses new solutions when standards can be improved upon

Brings new ideas to the framework, not just more steps/complexity

Opens up new avenues to explore and new applications to other problems

“It was very hard to select the winner, because the work was so terrific across the board, ” Robert said. “However, what the winner did this year that impressed us was first used text mining to discover features of the diagnostic codes, and second modeled the outcome in two stages (response, visit margin) to potentially uncover richer relationships.”